AMR Parsing with Action-Pointer Transformer
Jiawei Zhou, Tahira Naseem, et al.
NAACL 2021
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scientific Discovery (GT4SD). This extensible open-source library enables scientists, developers, and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design.
Jiawei Zhou, Tahira Naseem, et al.
NAACL 2021
Vinamra Baghel, Ayush Jain, et al.
INFORMS 2023
Baifeng Shi, Judy Hoffman, et al.
NeurIPS 2020
Philippe Schwaller, Benjamin Hoover, et al.
Science Advances